Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power-grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long- and short-period characteristics?” is addressed. The study focuses on the long-term variability of measured GIC, and establishes the extent to which the variability relates to quiet-time geomagnetic activity. GIC quiet-day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of Thermosphere-Ionosphere- Electrodynamics General Circulation Model (TIE-GCM)-simulated neutral wind and height-integrated current density. The results show strong evidence that the middle-latitude nodes routinely respond to the tidal-driven Sq variation, with a local time and seasonal dependence on the the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC-based significance score computed in this study provides power utilities with a node-specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.

This paper analyses magnetosphere-ionosphere (MI) coupling from a perspective that is independent of inertial reference frame, explicitly acknowledging the role of special relativity in MI coupling. We review the theory of special relativity in the context of MI coupling, and discuss how the MI coupling literature has used a particular low-velocity limit of special relativity known as the “magnetic limit”. We discuss how purely electrodynamic approaches to MI-coupling, where the high latitude electric field plays a central role, depend on inertial reference frame, so descriptions of MI-coupling involving the electric field depend on what reference frame is used to build the physical description. Choosing different reference frames leads to different descriptions of the physics, and essential physics common to all reference frames may be missed by tying the physical description to a specific reference frame. Reference frame-independent descriptions require that ion-neutral relative velocities and ion-neutral collisions are central to MI-coupling. Yet, the literature contains several examples of MI coupling theories that ignore the neutrals and focus instead on the electric field. Whereas neutral wind effects have been reported to modify electrodynamic effects such as Joule heating by ~25%, we show that the consequences of relative motion between ions and neutrals result in much larger impacts for significant geomagnetic storms when ion-neutral velocity differences are largest near the initiation of large-scale ion convection.

The magnetosphere, ionosphere and thermosphere (MIT) act as a coherently integrated system (geospace), driven in part by solar influences and characterized by variability and complexity. Among the most important and yet uncertain aspects of the geospace system is energy and momentum coupling between regions, which is, in part, accomplished by the transfer of charged particles from the magnetosphere to the ionosphere in a process known as particle precipitation, and in the opposite direction by ion outflow. Both processes are inherently multiscale and manifest the variabilities and complexities of the geospace system. Despite the importance of the transfer of particles, existing models are increasingly ill-equipped to provide the specification necessary for the growing demand for geospace now- and forecasts. Due to recent trends in the availability of data, we now face an exciting opportunity to progress particle transfer in geospace through the intersection of traditional approaches and state-of-the-art data-driven sciences. We reveal novel particle transfer models utilizing machine learning (ML), present results from the models, and provide an evaluation of their capabilities including comparisons with observations and the current ’state-of-the-art’ models (e.g., OVATION Prime for particle precipitation and the Gamera-Ionosphere Polar Wind Model for ion outflow). We detail the data wrangling required to utilize the available geospace observations to make progress on the long-standing challenge of particle transfer and place specific emphasis on the discovery possible when ML models are appropriate and robustly interrogated in the context of physical understanding. Our presentation helps illustrate the trends in the application of data science in space science.

The growing depth and breadth of available data that span the solar-terrestrial environment place us at a tipping point – the potential of these data is immense but realizing that potential requires a new representation. A new network-based approach to represent data collected by power utilities along with information from the solar-terrestrial connection is used. The progress is generated as part of a new project within the National Science Foundation Convergence Accelerator program: “The Convergence Hub for the Exploration of Space Science (CHESS).” Results are shared from current data provided through the Electric Power Research Institute (EPRI) SUNBURST project linked to magnetometer data from the Super Magnetometer Initiative. These data are transformed into a network with GIC measurements and magnetometers as the nodes in order to answer a long-standing question: “How much more likely are deviations from the average current when there is active space weather”? To answer this question, periods of active space weather are identified in the magnetometer data, and these are compared to times of DC transients in the GIC data. The probability of a these transients is found to be , on average, 1.6 times higher during periods of active space weather than during quiet times. The most indicative magnetometers of these DC transients are often not the closest to where the GICs are measured.